Spatial Diversity Deconstruct of C-band Scatter Components in Multistatic RaDaR Datasets using Machine Learning Techniques

In complex application wherein the signal propagating through free space is subject to multipath interference due to scatter by line-of-sight and non-line-of-sight objects in the propagation channel. The aims is to identify scatter centers in the propagation channel and characterize them based on their subjective characteristics, interpreted based on machine learning algorithm operations. Data-driven models are employed, replacing the traditional analytical approaches, in order to profile the scatter centers as either of absorbing or reflecting types based on the manner in which the signals are affected. A typical multistatic detection scenario is reconstructed under controlled laboratory conditions in order to create spatially independent data sets, while operating in the C-band frequency. The outcomes of this study are then applied to identify the scatter centers based on the distinct signatures they register in the experimental data set. As a converse argument, the process of antenna pattern estimation can now be performed free of an anechoic chamber setup, which is time and cost insensitive. A greater relevance shall be in the context of mid-band 5G-NR cellular communication systems that need to optimize the distributed antenna location attributes on time and cost constrained scales before attempting a large-scale deployment.